In the progressive GAN architecture
[1]}, layers are added to both the generator and the discriminator during training.
The addition of new layers increases the fine detail as training progresses.
A random noise vector is given as an input to the generator. It is possible to have an embedding layer [2]} at the input of the Progressive GAN which provides class conditional information to the network. However, even with the class information, the generated image will be random in nature with no control over the generated structure or texture of the image.
